Although pipelines are the safest method to transport fuels, they are associated with risks due to failures, leading to significant negative consequences. This paper investigates pipeline accident data provided by PHMSA (Pipeline and Hazardous Material Safety Administration) between 2010 and 2017, with a focus on environmental consequences of hazardous liquid pipeline accidents. The average amount of released product, the average time elapsed between the accident, the emergency response from the oil company, and the average costs of environmental remediation are estimated. The impact on soil, water, and wildlife is investigated for frequency and magnitude, where possible. It was found that, on average, 85% of product released after an accident remained unrecovered, 53% of accidents led to soil contamination, 41% of accidents impacted environmentally sensitive areas, and 92% of water crossing pipelines involved in accidents were uncased. From an annual average total cost of USD 326 million, annual average environmental damage and remediation costs were USD 140 million. This analysis assists in the diagnosis of challenges that might be addressed with improved maintenance and inspection programs, especially for pipelines at higher risk of negative environmental consequences. Finally, the performance of safety management systems should be improved to efficiently respond to emergencies.
Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. This study proposes a novel technique to forecast flows that use a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) along the Athabasca River in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, gauging data measured near the source were used to predict river flow near the mouth, over approximately 1000 km. The performance of this technique was compared to nonsequential and multi-input ANFISs, which use gauging data measured at each of the four hydrometric stations. The results show that a sequential ANFIS can accurately predict river flow (r2 = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) by using a single input, compared to nonsequential and multi-input ANFIS (2 days). This method provides accurate predictions over large distances, allowing for flow forecasts over longer periods of time. Therefore, governmental agencies and community planners could utilize this technique to improve flood prevention and planning, operations, maintenance, and the administration of water resource systems.
Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (ENS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle.
Pipelines are among the safest methods to transport oil and gas, but when an incident occurs, it can lead to disaster. Pipeline failures often cause injuries, fatalities, explosions and fires due to product ignition, property damage, and spills that can lead to environmental impact. The likelihood and consequence analyses of pipeline failures from past events are necessary for the development of realistic risk models. For this reason, a statistical analysis of failure consequences between 2010 and 2015 based on the Pipeline and Hazardous Materials Safety Administration (PHMSA) database is provided in this paper. Relationships between the pipeline failure consequences and the basic pipeline design variables are investigated. They provide a valuable contribution to pipeline risk modeling. Results show that recently installed hazardous liquid pipelines of large diameters and high operating pressure are more likely to cause ignitions. In contrast, older installed hazardous liquid pipelines of small diameters cause larger release volumes and more expensive property damages. The portion of fatalities and injuries that is caused by distribution pipeline accidents is higher for the public than workers compared to other pipeline types.
In Canada, the National Energy Board (NEB) regulates inter-provincial oil and gas pipelines and maintains historical records that contain data on oil and gas pipeline accidents; these data include information about operators, the accidents’ cause, and the resulting consequences. New inter-provincial pipelines are being built in Canada to transport fuels, but no comprehensive statistical analysis of the risk to environmental receptors exists. This study assesses the quality and quantity of NEB pipeline failure data available in Canada with a focus on environmental consequences and investigates differences between Canada and a more thoroughly studied jurisdiction, the United States, in tracking accident data. The discrepancies in agencies’ jurisdiction and regulated mileage are analyzed, along with reporting criteria and initial recording year. The level of detail provided by the two agencies is compared, identifying deficiencies in data collection. The Pipeline and Hazardous Materials Safety Administration (PHMSA) regulates 76% of pipelines in the United States, whereas the NEB only monitors 9% of pipelines in Canada. PHMSA provides four databases that include accidents from the 1980s for most pipelines and from 2011 for liquefied natural gas facilities; the NEB database includes accident data starting from 2008, which derive primarily from transmission pipelines. Information about environmental consequences is quite detailed in the US database, which reports 21 descriptive fields, whereas in Canada only two NEB database fields describe environmental outcomes. Moreover, dissimilarities in accident reporting criteria prevent the combination of data from the two agencies. Consequently, the NEB database does not allow for statistically robust and system wide analysis of the environmental consequences of pipeline failures in Canada. Furthermore, to calculate failure rates (annual number of accidents per kilometre of pipeline) for regulated pipelines, annual total mileage estimates are required. Mileage per year is provided by PHMSA for gas gathering, transmission, and distribution pipelines starting from 1984, and for hazardous liquid pipelines from 2004; the NEB provides annual mileage from 2010, a shorter period of record. The Canadian federal agencies are encouraged to improve accuracy and consistency in recording past accidents and in collecting pipeline data, with the goal of preventing and minimizing future pipeline failures.
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